TradeMate Dev
7b62c2f8b4
## H5 底部导航修复 (Bug #10) - 精简 App.vue，移除重复 tabbar，仅保留全局样式 - uni-page 设置 height: calc(100% - 50px) + overflow-y: auto - 内容区域精确停在底部导航上方，独立滚动不再叠加 - 恢复 custom-tab-bar 组件 ## 项目进度文档 - PROGRESS.md 更新至 10 个 Bug 修复 - 新增 H5 底部导航修复记录 - 新增历史变更条目
88 lines
4.0 KiB
Python
88 lines
4.0 KiB
Python
from typing import Dict, Any, Optional
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import json
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from anthropic import AsyncAnthropic
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from app.ai.base import AIProvider
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SYSTEM_PROMPTS = {
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"marketing": "You are a world-class copywriter for international trade. Write persuasive, "
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"culturally-adapted marketing content that converts. You excel at storytelling "
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"and emotional appeal in business contexts.",
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"reply": "You are a senior international sales representative with 20 years of experience. "
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"Your replies are warm, professional, and strategically move the conversation "
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"toward closing the deal.",
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"translate": "You are a professional translator specializing in trade documents. "
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"Preserve all numbers, terms, and formatting. Translate meaning, not words.",
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"extract": "Extract structured data from text. Return ONLY valid JSON.",
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}
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class ClaudeProvider(AIProvider):
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def __init__(self, api_key: str, model: str = "claude-sonnet-4-20250514"):
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self.client = AsyncAnthropic(api_key=api_key)
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self.model = model
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self._name = f"claude-sonnet"
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self._pricing = {"input": 0.003, "output": 0.015}
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async def translate(self, text: str, source_lang: Optional[str], target_lang: str, context: Optional[str] = None) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["translate"]
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if context:
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system += f"\nContext: {context}"
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prompt = f"Translate to {target_lang}:\n\n{text}"
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content = await self._call(system, prompt)
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return {"translated_text": content, "provider": self.name}
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async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional", preference_context: Optional[str] = None) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["reply"]
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if preference_context:
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system += f"\nUser writing preference: {preference_context}"
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context_str = ""
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if context:
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for k, v in context.items():
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if v:
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context_str += f"{k}: {v}\n"
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prompt = f"{context_str}\nCustomer says:\n{inquiry}\n\nYour reply ({tone} tone):"
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content = await self._call(system, prompt)
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return {"reply": content, "provider": self.name}
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async def generate_marketing(self, product_info: Dict[str, Any], target: str, style: str = "professional", language: str = "en", preference_context: Optional[str] = None) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["marketing"]
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if preference_context:
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system += f"\nUser preference: {preference_context}"
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info = json.dumps(product_info, ensure_ascii=False, indent=2)
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prompt = f"Product:\n{info}\n\nTarget: {target}\nStyle: {style}\nLanguage: {language}\n\nWrite marketing copy:"
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content = await self._call(system, prompt, max_tokens=1500)
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return {"content": content, "provider": self.name}
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async def extract_info(self, text: str, schema: Dict[str, Any]) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["extract"]
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prompt = f"Schema:\n{json.dumps(schema, indent=2)}\n\nText:\n{text}\n\nJSON:"
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content = await self._call(system, prompt, max_tokens=1000)
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try:
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data = json.loads(content)
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return {"data": data, "confidence": 0.9, "provider": self.name}
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except json.JSONDecodeError:
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return {"data": {}, "confidence": 0.0, "provider": self.name, "error": "parse_failed"}
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async def _call(self, system: str, prompt: str, max_tokens: int = 1000) -> str:
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resp = await self.client.messages.create(
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model=self.model,
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system=system,
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messages=[{"role": "user", "content": prompt}],
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max_tokens=max_tokens,
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temperature=0.7,
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)
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return resp.content[0].text
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@property
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def name(self) -> str:
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return self._name
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@property
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def cost_per_1k_tokens(self) -> float:
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return (self._pricing["input"] + self._pricing["output"]) / 2
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@property
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def supports_streaming(self) -> bool:
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return True
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